## Load objects with the posterior distributions using non-informative priors

load(here("final_analyses", "output", "data",
                 "noninformative_recovery_death.RData"))

## Load objects with rma() outputs for forest plots

load(here("final_analyses", "output", "data",
                 "priors_death.RData"))

## Load objects with mean + SD of multiple priors along with
## RECOVERY data and posterior distributions

load(file = here("final_analyses", "output", "data",
                 "multiple_priors_death.RData"))

## Load objets with all the posterior for summary plot

load(here("final_analyses", "output", "data",
                 "all_posteriors_death.RData"))

## Load functions to plot

# To create summary table

source(here("final_analyses", "script", "functions", # file path
            "summary_table_death.R"))               # file name


# To plot risk distributions side-by-side

source(here("final_analyses", "script", "functions", # file path
            "risk_comparison_plot.R"))               # file name

# To plot risk difference distribution

source(here("final_analyses", "script", "functions", # file path
            "risk_difference_plot.R"))               # file name

# To plot cumulative probability of risk difference

source(here("final_analyses", "script", "functions", # file path
            "cumulative_risk_difference_plot.R"))    # file name

# To plot prior, data and posterior distributions side-by-side

source(here("final_analyses", "script", "functions", # file path
            "data_prior_posterior_plot.R"))          # file name

# To plot the posterior distribution

source(here("final_analyses", "script", "functions", # file path
            "posterior_difference_plot.R"))          # file name

# To plot the cumulative probability of the posterior distribution

source(here("final_analyses", "script", "functions", # file path
            "posterior_cumulative_plot.R"))          # file name

Summary

As stated in our pre-registered protocol: “The skeptical prior uses the evidence-based prior (EBP)’s variance, but the mean equals \(0\). The optimistic prior uses the EBP’s variance, but the mean equals \(-0.05\). Lastly, the pessimistic prior uses the EBP’s variance, but the mean equals \(0.05\).”

Regarding the mortality outcome, negative risk differences mean benefit.

# X axis
xlim_inf = -12.5
xlim_sup = 12.5
ticks_spacing = 2.5

# Assure subgroup order
# https://stackoverflow.com/questions/12774210/
# how-do-you-specifically-order-ggplot2-x-axis-instead-of-alphabetical-order

level_order = c("Pessimistic",
                "Optimistic",
                "Skeptical",
                "Evidence-based",
                "Non-informative")

p1 = all_posteriors_simple_oxygen_death %>%
  
  # make it tidy for ggplot
  pivot_longer(
    cols = c("Non-informative":"Pessimistic"),
    names_to = "dist", values_to = "samples"
  ) %>%
  
  ggplot(aes(x = 100 * samples, y = factor(dist, level = level_order))) +

  # https://mjskay.github.io/ggdist/articles/slabinterval.html
  stat_halfeye(aes(fill = stat(x < 0)),
               .width = c(0.5, 0.95)
  ) +
  scale_fill_manual(values = c("gray90", "skyblue")) +
  labs(x = "\nRisk difference (%)",
       y = "Underlying prior\n",
       title = "Simple oxygen only subgroup\n"
  ) +
  scale_x_continuous(breaks = seq(from = xlim_inf, to = xlim_sup, ticks_spacing)) +
  scale_y_discrete(expand = c(0, 0.3)) +
  theme(
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.background = element_blank(),
    panel.grid.major.x = element_line(color = "gray80", size = 0.3),
    legend.position = "none",
    plot.title.position = 'plot',
    plot.margin = margin(20, 25, 10, 20)
  ) +
  coord_cartesian(xlim = c(xlim_inf, xlim_sup))

p2 = all_posteriors_noninvasive_death %>%
  
  # make it tidy for ggplot
  pivot_longer(
    cols = c("Non-informative":"Pessimistic"),
    names_to = "dist", values_to = "samples"
  ) %>%
  
  ggplot(aes(x = 100 * samples, y = factor(dist, level = level_order))) +

  # https://mjskay.github.io/ggdist/articles/slabinterval.html
  stat_halfeye(aes(fill = stat(x < 0)),
               .width = c(0.5, 0.95)
  ) +
  scale_fill_manual(values = c("gray90", "forestgreen")) +
  labs(x = "\nRisk difference (%)",
       y = "Underlying prior\n",
       title = "Non-invasive ventilation subgroup\n"
  ) +
  scale_x_continuous(breaks = seq(from = xlim_inf, to = xlim_sup, ticks_spacing)) +
  scale_y_discrete(expand = c(0, 0.3)) +
  theme(
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.background = element_blank(),
    panel.grid.major.x = element_line(color = "gray80", size = 0.3),
    legend.position = "none",
    plot.title.position = 'plot',
    plot.margin = margin(20, 25, 10, 20)
  ) +
  coord_cartesian(xlim = c(xlim_inf, xlim_sup))

p3 = all_posteriors_invasive_death %>%
  
  # make it tidy for ggplot
  pivot_longer(
    cols = c("Non-informative":"Pessimistic"),
    names_to = "dist", values_to = "samples"
  ) %>%
  
  ggplot(aes(x = 100 * samples, y = factor(dist, level = level_order))) +

  # https://mjskay.github.io/ggdist/articles/slabinterval.html
  stat_halfeye(aes(fill = stat(x < 0)),
               .width = c(0.5, 0.95)
  ) +
  scale_fill_manual(values = c("gray90", "firebrick")) +
  labs(x = "\nRisk difference (%)",
       y = "Underlying prior\n",
       title = "Invasive mechanical ventilation subgroup\n"
  ) +
  scale_x_continuous(breaks = seq(from = xlim_inf, to = xlim_sup, ticks_spacing)) +
  scale_y_discrete(expand = c(0, 0.3)) +
  theme(
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.background = element_blank(),
    panel.grid.major.x = element_line(color = "gray80", size = 0.3),
    legend.position = "none",
    plot.title.position = 'plot',
    plot.margin = margin(20, 25, 10, 20)
  ) +
  coord_cartesian(xlim = c(xlim_inf, xlim_sup))

p1 + plot_annotation(title = "Posterior distributions on mortality",
                          theme = theme(plot.title = element_text(size = 20)))

Interval bars depict 50% and 95% credible intervals

# Summary table
table_posterior = summary_table_death(all_posteriors_simple_oxygen_death)

# Use kableExtra to have a nice table

table_posterior %>% 
  kbl(booktabs = T, align = 'c') %>% 
  kable_styling(bootstrap_options = "striped", full_width = F) %>% 
  column_spec(7, bold = T, color = "#5891BF") %>%
  add_header_above(c(" " = 3,
                     "Probability of Benefit" = 4,
                     "Probability of Harm" = 3)) %>% 
  footnote(general = "RD = Risk Difference; SD = Standard Deviation")

			Probability of Benefit				Probability of Harm
Prior	Mean RD	SD of RD	Pr(< -5%)	Pr(< -2%)	Pr(< -1%)	Pr(< 0%)	Pr(> 1%)	Pr(> 2%)	Pr(> 5%)
Non-informative	-0.037	0.019	24	81	92	97	1	0	0
Evidence-based	-0.022	0.017	6	56	76	90	3	1	0
Skeptical	-0.032	0.018	15	75	89	97	1	0	0
Optimistic	-0.039	0.018	26	85	95	99	0	0	0
Pessimistic	-0.025	0.018	8	62	81	92	2	0	0
Note:
RD = Risk Difference; SD = Standard Deviation

p2

Interval bars depict 50% and 95% credible intervals

# Summary table
table_posterior = summary_table_death(all_posteriors_noninvasive_death)

# Use kableExtra to have a nice table

table_posterior %>% 
  kbl(booktabs = T, align = 'c') %>% 
  kable_styling(bootstrap_options = "striped", full_width = F) %>% 
  column_spec(7, bold = T, color = "forestgreen") %>%
  add_header_above(c(" " = 3,
                     "Probability of Benefit" = 4,
                     "Probability of Harm" = 3)) %>%  
  footnote(general = "RD = Risk Difference; SD = Standard Deviation")

			Probability of Benefit				Probability of Harm
Prior	Mean RD	SD of RD	Pr(< -5%)	Pr(< -2%)	Pr(< -1%)	Pr(< 0%)	Pr(> 1%)	Pr(> 2%)	Pr(> 5%)
Non-informative	-0.044	0.024	39	84	92	97	1	0	0
Evidence-based	-0.044	0.021	38	87	95	98	0	0	0
Skeptical	-0.033	0.021	21	74	87	95	2	1	0
Optimistic	-0.045	0.021	41	89	96	99	0	0	0
Pessimistic	-0.021	0.021	8	52	70	84	7	2	0
Note:
RD = Risk Difference; SD = Standard Deviation

p3

Interval bars depict 50% and 95% credible intervals.

# Summary table
table_posterior = summary_table_death(all_posteriors_invasive_death)

# Use kableExtra to have a nice table

table_posterior %>% 
  kbl(booktabs = T, align = 'c') %>% 
  kable_styling(bootstrap_options = "striped", full_width = F) %>% 
  column_spec(7, bold = T, color = "firebrick") %>%
  add_header_above(c(" " = 3,
                     "Probability of Benefit" = 4,
                     "Probability of Harm" = 3)) %>% 
  footnote(general = "RD = Risk Difference; SD = Standard Deviation")

			Probability of Benefit				Probability of Harm
Prior	Mean RD	SD of RD	Pr(< -5%)	Pr(< -2%)	Pr(< -1%)	Pr(< 0%)	Pr(> 1%)	Pr(> 2%)	Pr(> 5%)
Non-informative	-0.018	0.042	22	48	57	66	25	18	5
Evidence-based	-0.022	0.032	19	53	65	76	16	9	1
Skeptical	-0.010	0.032	10	38	50	63	26	17	3
Optimistic	-0.032	0.032	28	64	75	84	9	5	1
Pessimistic	0.011	0.032	3	16	25	36	51	39	11
Note:
RD = Risk Difference; SD = Standard Deviation

Per subgroup

Simple oxygen only

Non-informative prior

## Plot!

# I will use my own functions to standardize my plots

risk_comparison_plot(
  noninformative_recovery_simple_oxygen_death, # Data object

  ### start using quotes
  
  "gray50", # Color code for control group
  "#D3A464", # Color code for other group
  "\nRisk (%)", # X axis label
  "RECOVERY trial", # Title
  "Simple oxygen only subgroup\n", # Subtitle
  
  ### stop using quotes

  10, # X axis inferior limit
  30, # X axis superior limit
  2 # X axis spacing for ticks
)

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlabel = "\nRisk difference (%)"
xlim_inf = -10
xlim_sup = 5

p1 = risk_difference_plot(
  noninformative_recovery_simple_oxygen_death, # Data object

  ## start using quotes
  "#7EBAC2", # fill color code
  xlabel, # X axis label
  ### stop using quotes

  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2 # X axis label
) 

p2 = cumulative_risk_difference_plot(
  noninformative_recovery_simple_oxygen_death, # Data object
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  1, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
  )

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Non-informative prior",
  subtitle = "Risk difference between groups on simple oxygen only"
)

The interval bar depicts the 95% credible interval. The cumulative posterior distribution corresponds to the probabilities that the risk difference (RD) is less than or equal to the effect size on the X‐axis.

Evidence-based prior

forest(priors_simple_oxygen_death)

xlabel = "\nRisk difference (%)"

data_prior_posterior_plot(
  multiple_priors_simple_oxygen_death %>% # Output from normal_approximation()
    filter(type == "evidence-based"), 
  
  # start using quotes
           "#364D55", # Color for the prior
           "#7EBAC2", # Color for RECOVERY
           "#A65041", # Color for the posterior
           xlabel, # X axis label
           "Posterior distribution: RECOVERY + Evidence-based prior", # Title
           "Simple oxygen only subgroup", # Subtitle
           # stop using quotes
           -10, # X axis inferior limit
           20, # X axis superior limit
           5 # Spacing between ticks in X axis
  )

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlim_inf = -8
xlim_sup = 4

p1 = posterior_difference_plot(
  multiple_priors_simple_oxygen_death %>% # Output from normal_approximation()
    filter(type == "evidence-based"), 

  ## start using quotes
  "#A65041", # fill color code
  xlabel, # X axis label
  ### stop using quotes

  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2 # Spacing between ticks in X axis
) 

p2 = posterior_cumulative_plot(
  multiple_priors_simple_oxygen_death %>% # Output from normal_approximation()
    filter(type == "evidence-based"), 
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  1, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
  )

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Evidence-based prior",
  subtitle = "Simple oxygen only subgroup"
)

Skeptical prior

xlabel = "\nRisk difference (%)"

data_prior_posterior_plot(
  
  # Output from normal_approximation_multiple_priors()
  multiple_priors_simple_oxygen_death %>%
    filter(type == "skeptical"), 
  
  # start using quotes
           "gray60", # Color for the prior
           "#7EBAC2", # Color for RECOVERY
           "#A65041", # Color for the posterior
           xlabel, # X axis label
           "Posterior distribution: RECOVERY trial + Skeptical prior", # Title
           "Simple oxygen only subgroup", # Subtitle
           # stop using quotes
           -10, # X axis inferior limit
           10, # X axis superior limit
           5 # Spacing between ticks in X axis
  )

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlim_inf = -8
xlim_sup = 4

p1 = posterior_difference_plot(
  
  multiple_priors_simple_oxygen_death %>% # Output from normal_approximation()
    filter(type == "skeptical"), 

  ## start using quotes
  "gray50", # fill color code
  xlabel, # X axis label
  ### stop using quotes

  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2 # Spacing between ticks in X axis
) 

p2 = posterior_cumulative_plot(
  
  multiple_priors_simple_oxygen_death %>% # Output from normal_approximation()
    filter(type == "skeptical"), 
  
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  1, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
  )

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Skeptical prior",
  subtitle = "Simple oxygen only subgroup"
)

Optimistic prior

xlabel = "\nRisk difference (%)"

data_prior_posterior_plot(
  
  # Output from normal_approximation_multiple_priors()
  multiple_priors_simple_oxygen_death %>%
    filter(type == "optimistic"), 
  
  # start using quotes
  "#5CA881", # Color for the prior
  "#7EBAC2", # Color for RECOVERY
  "#A65041", # Color for the posterior
  xlabel, # X axis label
  "Posterior distribution: RECOVERY trial + Optimistic prior", # Title
  "Simple oxygen only subgroup", # Subtitle
  # stop using quotes
  -15, # X axis inferior limit
  5, # X axis superior limit
  5 # Spacing between ticks in X axis
)

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlim_inf = -10
xlim_sup = 2

p1 = posterior_difference_plot(
  
  multiple_priors_simple_oxygen_death %>% # Output from normal_approximation()
    filter(type == "optimistic"), 
  
  ## start using quotes
  "#5CA881", # fill color code
  xlabel, # X axis label
  ### stop using quotes
  
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2 # Spacing between ticks in X axis
) 

p2 = posterior_cumulative_plot(
  
  multiple_priors_simple_oxygen_death %>% # Output from normal_approximation()
    filter(type == "optimistic"), 
  
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  1, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
)

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Optimistic prior",
  subtitle = "Simple oxygen only subgroup"
)

Pessimistic prior

xlabel = "\nRisk difference (%)"

data_prior_posterior_plot(
  
  # Output from normal_approximation_multiple_priors()
  multiple_priors_simple_oxygen_death %>%
    filter(type == "pessimistic"), 
  
  # start using quotes
  "#523C84", # Color for the prior
  "#7EBAC2", # Color for RECOVERY
  "#A65041", # Color for the posterior
  xlabel, # X axis label
  "Posterior distribution: RECOVERY trial + Pessimistic prior", # Title
  "Simple oxygen only subgroup", # Subtitle
  # stop using quotes
  -10, # X axis inferior limit
  15, # X axis superior limit
  5 # Spacing between ticks in X axis
)

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlim_inf = -8
xlim_sup = 4

p1 = posterior_difference_plot(
  
  multiple_priors_simple_oxygen_death %>% # Output from normal_approximation()
    filter(type == "pessimistic"), 
  
  ## start using quotes
  "#523C84", # fill color code
  xlabel, # X axis label
  ### stop using quotes
  
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2 # Spacing between ticks in X axis
) 

p2 = posterior_cumulative_plot(
  
  multiple_priors_simple_oxygen_death %>% # Output from normal_approximation()
    filter(type == "pessimistic"), 
  
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  1, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
)

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Pessimistic prior",
  subtitle = "Simple oxygen only subgroup"
)

Non-invasive ventilation

Non-informative prior

## Plot!

# I will be using my own functions to standardize my plots

risk_comparison_plot(
  noninformative_recovery_non_invasive_death, # Output from normal_approximation()

  ### start using quotes
  
  "gray50", # Color code for control group
  "#D3A464", # Color code for other group
  "\nRisk (%)", # X axis label
  "RECOVERY trial", # Title
  "Non-invasive ventilation subgroup\n", # Subtitle
  
  ### stop using quotes

  28, # X axis inferior limit
  48, # X axis superior limit
  2 # Spacing between ticks in X axis
)

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlabel = "\nRisk difference (%)"
xlim_inf = -12
xlim_sup = 5

p1 = risk_difference_plot(
  noninformative_recovery_non_invasive_death, # Data object

  ## start using quotes
  "#7EBAC2", # fill color code
  xlabel, # X axis label
  ### stop using quotes

  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2 # Spacing between ticks in X axis
) 

p2 = cumulative_risk_difference_plot(
  noninformative_recovery_non_invasive_death, # Data object
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  1, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
  )

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Non-informative prior",
  subtitle = "Risk difference between groups on non-invasive ventilation"
)

Evidence-based prior

forest(priors_noninvasive_death)

data_prior_posterior_plot(
   multiple_priors_noninvasive_death %>%
    filter(type == "evidence-based"), # Output from normal_approximation()
  
  # start using quotes
           "#364D55", # Color for the prior
           "#7EBAC2", # Color for RECOVERY
           "#A65041", # Color for the posterior
           xlabel, # X axis label
           "Posterior distribution: RECOVERY + Evidence-based prior", # Title
           "Non-invasive ventilation subgroup", # Subtitle
           # stop using quotes
           -15, # X axis inferior limit
           5, # X axis superior limit
           5 # Spacing between ticks in X axis
  )

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlim_inf = -12
xlim_sup = 4

p1 = posterior_difference_plot(
  multiple_priors_noninvasive_death %>% # Output from normal_approximation()
    filter(type == "evidence-based"), 

  ## start using quotes
  "#A65041", # fill color code
  xlabel, # X axis label
  ### stop using quotes

  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2 # Spacing between ticks in X axis
) 

p2 = posterior_cumulative_plot(
  multiple_priors_noninvasive_death %>% # Output from normal_approximation()
    filter(type == "evidence-based"), 
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  1, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
  )

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Evidence-based prior",
  subtitle = "Non-invasive ventilation subgroup"
)

Skeptical prior

xlabel = "\nRisk difference (%)"

data_prior_posterior_plot(
  
  # Output from normal_approximation_multiple_priors()
  multiple_priors_noninvasive_death %>%
    filter(type == "skeptical"), 
  
  # start using quotes
           "gray60", # Color for the prior
           "#7EBAC2", # Color for RECOVERY
           "#A65041", # Color for the posterior
           xlabel, # X axis label
           "Posterior distribution: RECOVERY trial + Skeptical prior", # Title
           "Non-invasive ventilation subgroup", # Subtitle
           # stop using quotes
           -10, # X axis inferior limit
           10, # X axis superior limit
           5 # Spacing between ticks in X axis
  )

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlim_inf = -10
xlim_sup = 4

p1 = posterior_difference_plot(
  
  multiple_priors_noninvasive_death %>% # Output from normal_approximation_multiple_priors()
    filter(type == "skeptical"), 

  ## start using quotes
  "gray50", # fill color code
  xlabel, # X axis label
  ### stop using quotes

  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2 # Spacing between ticks in X axis
) 

p2 = posterior_cumulative_plot(
  
  multiple_priors_noninvasive_death %>% # Output from normal_approximation_multiple_priors()
    filter(type == "skeptical"), 
  
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  1, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
  )

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Skeptical prior",
  subtitle = "Non-invasive ventilation subgroup"
)

Optimistic prior

xlabel = "\nRisk difference (%)"

data_prior_posterior_plot(
  
  # Output from normal_approximation_multiple_priors()
  multiple_priors_noninvasive_death %>%
    filter(type == "optimistic"), 
  
  # start using quotes
  "#5CA881", # Color for the prior
  "#7EBAC2", # Color for RECOVERY
  "#A65041", # Color for the posterior
  xlabel, # X axis label
  "Posterior distribution: RECOVERY trial + Optimistic prior", # Title
  "Non-invasive ventilation subgroup", # Subtitle
  # stop using quotes
  -15, # X axis inferior limit
  5, # X axis superior limit
  5 # Spacing between ticks in X axis
)

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlim_inf = -10
xlim_sup = 2

p1 = posterior_difference_plot(
  
  multiple_priors_noninvasive_death %>% # Output from normal_approximation_multiple_priors()
    filter(type == "optimistic"), 
  
  ## start using quotes
  "#5CA881", # fill color code
  xlabel, # X axis label
  ### stop using quotes
  
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2 # Spacing between ticks in X axis
) 

p2 = posterior_cumulative_plot(
  
  multiple_priors_noninvasive_death %>% # Output from normal_approximation_multiple_priors()
    filter(type == "optimistic"), 
  
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  1, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
)

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Optimistic prior",
  subtitle = "Non-invasive ventilation subgroup"
)

Pessimistic prior

xlabel = "\nRisk difference (%)"

data_prior_posterior_plot(
  
  # Output from normal_approximation_multiple_priors()
  multiple_priors_noninvasive_death %>%
    filter(type == "pessimistic"), 
  
  # start using quotes
  "#523C84", # Color for the prior
  "#7EBAC2", # Color for RECOVERY
  "#A65041", # Color for the posterior
  xlabel, # X axis label
  "Posterior distribution: RECOVERY trial + Pessimistic prior", # Title
  "Non-invasive ventilation subgroup", # Subtitle
  # stop using quotes
  -10, # X axis inferior limit
  15, # X axis superior limit
  5 # Spacing between ticks in X axis
)

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlim_inf = -8
xlim_sup = 4

p1 = posterior_difference_plot(
  
  multiple_priors_noninvasive_death %>% # Output from normal_approximation_multiple_priors()
    filter(type == "pessimistic"), 
  
  ## start using quotes
  "#523C84", # fill color code
  xlabel, # X axis label
  ### stop using quotes
  
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2 # Spacing between ticks in X axis
) 

p2 = posterior_cumulative_plot(
  
  multiple_priors_noninvasive_death %>% # Output from normal_approximation_multiple_priors()
    filter(type == "pessimistic"), 
  
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  1, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
)

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Pessimistic prior",
  subtitle = "Non-invasive ventilation subgroup"
)

Invasive mechanical ventilation

Non-informative prior

## Plot!

# I will be using my own functions to standardize my plots

risk_comparison_plot(
  noninformative_recovery_invasive_death, # Data object

  ### start using quotes
  
  "gray50", # Color code for control group
  "#D3A464", # Color code for other group
  "\nRisk (%)", # X axis label
  "RECOVERY trial", # Title
  "Invasive mechanical ventilation subgroup\n", # Subtitle
  
  ### stop using quotes

  36, # X axis inferior limit
  58, # X axis superior limit
  2 # Spacing between ticks in X axis
) + 
  
  # Extra function to better limits the axis in this case
  coord_cartesian(c(36, 58))

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlabel = "\nRisk difference (%)"
xlim_inf = -16
xlim_sup = 12

p1 = risk_difference_plot(
  noninformative_recovery_invasive_death, # Data object

  ## start using quotes
  "#7EBAC2", # fill color code
  xlabel, # X axis label
  ### stop using quotes

  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  4 # Spacing between ticks in X axis
) 

p2 = cumulative_risk_difference_plot(
  noninformative_recovery_invasive_death, # Data object
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
  )

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Non-informative prior",
  subtitle = "Risk difference between groups on invasive mechanical ventilation"
)

Evidence-based prior

forest(priors_invasive_death)

data_prior_posterior_plot(
  multiple_priors_invasive_death %>%
    filter(type == "evidence-based"), # Output from normal_approximation_multiple_priors()
  
  # start using quotes
           "#364D55", # Color for the prior
           "#7EBAC2", # Color for RECOVERY
           "#A65041", # Color for the posterior
           xlabel, # X axis label
           "Posterior distribution: RECOVERY + Evidence-based prior", # Title
           "Invasive mechanical ventilation subgroup", # Subtitle
           # stop using quotes
           -15, # X axis inferior limit
           10, # X axis superior limit
           5 # Spacing between ticks in X axis
  )

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlabel = "\nRisk difference (%)"
xlim_inf = -12
xlim_sup = 8

p1 = posterior_difference_plot(
  multiple_priors_invasive_death %>% # Output from normal_approximation_multiple_priors()
    filter(type == "evidence-based"), 

  ## start using quotes
  "#A65041", # fill color code
  xlabel, # X axis label
  ### stop using quotes

  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2 # Spacing between ticks in X axis
) 

p2 = posterior_cumulative_plot(
  multiple_priors_invasive_death %>% # Output from normal_approximation_multiple_priors()
    filter(type == "evidence-based"), 
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
  )

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Evidence-based prior",
  subtitle = "Invasive mechanical ventilation subgroup"
)

Skeptical prior

xlabel = "\nRisk difference (%)"

data_prior_posterior_plot(
  
  # Output from normal_approximation_multiple_priors()
  multiple_priors_invasive_death %>%
    filter(type == "skeptical"), 
  
  # start using quotes
           "gray60", # Color for the prior
           "#7EBAC2", # Color for RECOVERY
           "#A65041", # Color for the posterior
           xlabel, # X axis label
           "Posterior distribution: RECOVERY trial + Skeptical prior", # Title
           "Invasive mechanical ventilation subgroup", # Subtitle
           # stop using quotes
           -10, # X axis inferior limit
           10, # X axis superior limit
           5 # Spacing between ticks in X axis
  )

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlim_inf = -12
xlim_sup = 8

p1 = posterior_difference_plot(
  
  multiple_priors_invasive_death %>% # Output from normal_approximation_multiple_priors()
    filter(type == "skeptical"), 

  ## start using quotes
  "gray50", # fill color code
  xlabel, # X axis label
  ### stop using quotes

  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  4 # Spacing between ticks in X axis
) 

p2 = posterior_cumulative_plot(
  
  multiple_priors_invasive_death %>% # Output from normal_approximation_multiple_priors()
    filter(type == "skeptical"), 
  
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
  )

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Skeptical prior",
  subtitle = "Invasive mechanical ventilation subgroup"
)

Optimistic prior

xlabel = "\nRisk difference (%)"

data_prior_posterior_plot(
  
  # Output from normal_approximation_multiple_priors()
  multiple_priors_invasive_death %>%
    filter(type == "optimistic"), 
  
  # start using quotes
  "#5CA881", # Color for the prior
  "#7EBAC2", # Color for RECOVERY
  "#A65041", # Color for the posterior
  xlabel, # X axis label
  "Posterior distribution: RECOVERY trial + Optimistic prior", # Title
  "Invasive mechanical ventilation subgroup", # Subtitle
  # stop using quotes
  -15, # X axis inferior limit
  10, # X axis superior limit
  5 # Spacing between ticks in X axis
)

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlim_inf = -12
xlim_sup = 8

p1 = posterior_difference_plot(
  
  multiple_priors_invasive_death %>% # Output from normal_approximation_multiple_priors()
    filter(type == "optimistic"), 
  
  ## start using quotes
  "#5CA881", # fill color code
  xlabel, # X axis label
  ### stop using quotes
  
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  4 # Spacing between ticks in X axis
) 

p2 = posterior_cumulative_plot(
  
  multiple_priors_invasive_death %>% # Output from normal_approximation_multiple_priors()
    filter(type == "optimistic"), 
  
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
)

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Optimistic prior",
  subtitle = "Invasive mechanical ventilation subgroup"
)

Pessimistic prior

xlabel = "\nRisk difference (%)"

data_prior_posterior_plot(
  
  # Output from normal_approximation_multiple_priors()
  multiple_priors_invasive_death %>%
    filter(type == "pessimistic"), 
  
  # start using quotes
  "#523C84", # Color for the prior
  "#7EBAC2", # Color for RECOVERY
  "#A65041", # Color for the posterior
  xlabel, # X axis label
  "Posterior distribution: RECOVERY trial + Pessimistic prior", # Title
  "Invasive mechanical ventilation subgroup", # Subtitle
  # stop using quotes
  -10, # X axis inferior limit
  15, # X axis superior limit
  5 # Spacing between ticks in X axis
)

While the darker shaded area depicts the 50% credible interval (CI), the interval bar depicts the 95% CI.

xlim_inf = -12
xlim_sup = 12

p1 = posterior_difference_plot(
  
  multiple_priors_invasive_death %>% # Output from normal_approximation_multiple_priors()
    filter(type == "pessimistic"), 
  
  ## start using quotes
  "#523C84", # fill color code
  xlabel, # X axis label
  ### stop using quotes
  
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  4 # Spacing between ticks in X axis
) 

p2 = posterior_cumulative_plot(
  
  multiple_priors_invasive_death %>% # Data object
    filter(type == "pessimistic"), 
  
  xlabel, # X axis label (in quotes)
  xlim_inf, # X axis inferior limit
  xlim_sup, # X axis superior limit
  2, # Spacing between ticks in X axis
  "mortality" # Outcome (within quotes)
)

p1 + p2 + plot_annotation(
  title = "Posterior distribution: RECOVERY trial + Pessimistic prior",
  subtitle = "Invasive mechanical ventilation subgroup"
)

sessionInfo()

## R version 4.0.4 (2021-02-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] kableExtra_1.3.4 metafor_2.4-0    Matrix_1.3-2     here_1.0.1      
##  [5] patchwork_1.1.1  ggdist_2.4.0     tidybayes_2.3.1  forcats_0.5.1   
##  [9] stringr_1.4.0    dplyr_1.0.5      purrr_0.3.4      readr_1.4.0     
## [13] tidyr_1.1.3      tibble_3.1.1     ggplot2_3.3.3    tidyverse_1.3.1 
## 
## loaded via a namespace (and not attached):
##  [1] httr_1.4.2            sass_0.3.1            viridisLite_0.4.0    
##  [4] jsonlite_1.7.2        rdocsyntax_0.4.1.9000 modelr_0.1.8         
##  [7] bslib_0.2.4           assertthat_0.2.1      distributional_0.2.2 
## [10] highr_0.9             cellranger_1.1.0      yaml_2.2.1           
## [13] pillar_1.6.0          backports_1.2.1       lattice_0.20-41      
## [16] glue_1.4.2            arrayhelpers_1.1-0    digest_0.6.27        
## [19] rvest_1.0.0           colorspace_2.0-0      htmltools_0.5.1.1    
## [22] plyr_1.8.6            pkgconfig_2.0.3       broom_0.7.6          
## [25] svUnit_1.0.3          haven_2.4.0           webshot_0.5.2        
## [28] scales_1.1.1          svglite_2.0.0         generics_0.1.0       
## [31] farver_2.1.0          ellipsis_0.3.1        cachem_1.0.4         
## [34] withr_2.4.1           cli_2.4.0             magrittr_2.0.1       
## [37] crayon_1.4.1          readxl_1.3.1          memoise_2.0.0        
## [40] evaluate_0.14         fs_1.5.0              fansi_0.4.2          
## [43] nlme_3.1-152          xml2_1.3.2            tools_4.0.4          
## [46] hms_1.0.0             lifecycle_1.0.0       V8_3.4.0             
## [49] munsell_0.5.0         reprex_2.0.0          compiler_4.0.4       
## [52] jquerylib_0.1.3       systemfonts_1.0.1     rlang_0.4.10         
## [55] grid_4.0.4            rstudioapi_0.13       rmarkdown_2.7        
## [58] gtable_0.3.0          DBI_1.1.1             curl_4.3             
## [61] R6_2.5.0              lubridate_1.7.10      knitr_1.32           
## [64] fastmap_1.1.0         utf8_1.2.1            rprojroot_2.0.2      
## [67] stringi_1.5.3         Rcpp_1.0.6            vctrs_0.3.7          
## [70] dbplyr_2.1.1          tidyselect_1.1.0      xfun_0.22            
## [73] coda_0.19-4

05_Posteriors_Mortality

Arthur M. Albuquerque

May 01, 2021

Summary

Per subgroup

Simple oxygen only

Non-informative prior

Evidence-based prior

Skeptical prior

Optimistic prior

Pessimistic prior

Non-invasive ventilation

Non-informative prior

Evidence-based prior

Skeptical prior

Optimistic prior

Pessimistic prior

Invasive mechanical ventilation

Non-informative prior

Evidence-based prior

Skeptical prior

Optimistic prior

Pessimistic prior